SEARCH · Engineering Papers

Results for “spatial”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 289 records · Page 16

All sources NASA NTRS DOE OSTI

DOE OSTI2023

Physics-Informed Deep Learning for Reconstruction of Spatial Missing Climate Information in the Antarctic

Understanding the influence of the Antarctic on the global climate is crucial for the prediction of global warming. However, due to very few observation sites, it is difficult to reconstruct the rational spatial pattern by filling in the missing values from the limited site observations. To tackle this challenge, regional spatial gap-filling methods, such as Kriging and inverse distance weighted (IDW), are regularly used in geoscience. Nevertheless, the reconstructing credibility of these methods is undesirable when the spatial structure has massive missing pieces. Inspired by image inpainting, we propose a novel deep learning method that demonstrates a good effect by embedding the physics-aware initialization of deep learning methods for rapid learning and capturing the spatial dependence for the high-fidelity imputation of missing areas. We create the benchmark dataset that artificially masks the Antarctic region with ratios of 30%, 50% and 70%. The reconstructing monthly mean surface temperature using the deep learning image inpainting method RFR (Recurrent Feature Reasoning) exhibits an average of 63% and 71% improvement of accuracy over Kriging and IDW under different missing rates. With regard to wind speed, there are still 36% and 50% improvements. In particular, the achieved improvement is even better for the larger missing ratio, such as under the 70% missing rate, where the accuracy of RFR is 68% and 74% higher than Kriging and IDW for temperature and also 38% and 46% higher for wind speed. In addition, the PI-RFR (Physics-Informed Recurrent Feature Reasoning) method we proposed is initialized using the spatial pattern data simulated by the numerical climate model instead of the unified average. Compared with RFR, PI-RFR has an average accuracy improvement of 10% for temperature and 9% for wind speed. When applied to reconstruct the spatial pattern based on the Antarctic site observations, where the missing rate is over 90%, the proposed method exhibits more spatial characteristics than Kriging and IDW.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

An Analysis of the Spatial Variations in the Relationship Between Built Environment and Severe Crashes

Traffic crashes significantly contribute to global fatalities, particularly in urban areas, highlighting the need to evaluate the relationship between urban environments and traffic safety. This study extends former spatial modeling frameworks by drawing paths between global models, including spatial lag (SLM), and spatial error (SEM), and local models, including geographically weighted regression (GWR), multi-scale geographically weighted regression (MGWR), and multi-scale geographically weighted regression with spatially lagged dependent variable (MGWRL). Utilizing the proposed framework, this study analyzes severe traffic crashes in relation to urban built environments using various spatial regression models within Leon County, Florida. According to the results, SLM outperforms OLS, SEM, and GWR models. Local models with lagged dependent variables outperform both the global and generic versions of the local models in all performance measures, whereas MGWR and MGWRL outperform GWR and GWRL. Local models performed better than global models, showing spatial non-stationarity; so, the relationship between the dependent and independent variables varies over space. The better performance of models with lagged dependent variables signifies that the spatial distribution of severe crashes is correlated. Finally, the better performance of multi-scale local models than classical local models indicates varying influences of independent variables with different bandwidths. According to the MGWRL model, census block groups close to the urban area with higher population, higher education level, and lower car ownership rates have lower crash rates. On the contrary, motor vehicle percentage for commuting is found to have a negative association with severe crash rate, which suggests the locality of the mentioned associations.

Alisan, Onur (ORCID:0000000193113984)↗

DOE OSTI2017

Soil respiration across a permafrost transition zone: spatial structure and environmental correlates

Soil respiration is a key ecosystem function whereby shifts in respiration rates can shift systems from carbon sinks to sources. Soil respiration in permafrost-associated systems is particularly important given climate change driven permafrost thaw that leads to significant uncertainty in resulting ecosystem carbon dynamics. Here we characterize the spatial structure and environmental drivers of soil respiration across a permafrost transition zone. We find that soil respiration is characterized by a non-linear threshold that occurs at active-layer depths greater than 140 cm. We also find that within each season, tree basal area is a dominant driver of soil respiration regardless of spatial scale, but only in spatial domains with significant spatial variability in basal area. Our analyses further show that spatial variation (the coefficient of variation) and mean-variance power-law scaling of soil respiration in our boreal system are consistent with previous work in other ecosystems (e.g., tropical forests) and in population ecology, respectively. Comparing our results to those in other ecosystems suggests that temporally stable features such as tree-stand structure are often primary drivers of spatial variation in soil respiration. If so, this provides an opportunity to better estimate the magnitude and spatial variation in soil respiration through remote sensing. Finally, combining such an approach with broader knowledge of thresholding behavior – here related to active layer depth – would provide empirical constraints on models aimed at predicting ecosystem responses to ongoing permafrost thaw.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Comprehension of Spatial Constraints by Neural Logic Learning from a Single RGB-D Scan

Autonomous industrial assembly relies on the precise measurement of spatial constraints as designed by computer-aided design (CAD) software such as SolidWorks. This paper proposes a framework for an intelligent industrial robot to understand the spatial constraints for model assembly. An extended generative adversary network (GAN) with a 3D long short-term memory (LSTM) network was designed to composite 3D point clouds from a single RGB-D scan. The spatial constraints of the segmented point clouds are identified by a neural-logic network that incorporates general knowledge of spatial constraints in terms of first-order logic. The model was designed to comprehend a complete set of spatial constraints that are consistent with industrial CAD software, including left, right, above, below, front, behind, parallel, perpendicular, concentric, and coincident relations. The accuracy of 3D model composition and spatial constraint identification was evaluated by the RGB-D scans and 3D models in the ABC dataset. The proposed model achieved 57.23% intersection over union (IoU) in 3D model composition, and over 99% in comprehending all spatial constraints.

Wang, Dali↗

DOE OSTI2021

Statistical analysis of spatial distribution of pores in metal additive manufacturing

We report mechanical properties of additively manufactured parts are sensitive to the presence of pores form during the manufacturing process. The impact of pores on the mechanical performance has been investigated extensively with respect to different parameters, such as pore volume fraction, shape, and size. However, statistical investigations focusing on the relationships between the spatial distribution of pores and process parameters; and consequently, the performance of the manufactured parts are scattered and limited. Also, these sparse investigations usually suffer from an ambiguous definition of terminologies. For instance, the required criteria to consider a point pattern as complete spatial randomness (CSR) are generally not clarified. Moreover, no numerical formalism is yet developed to show how much the observed spatial results are statistically significant. To address these shortcomings, the statistical definition of CSR in a point pattern and the procedure to quantitatively determine the deviation of a pattern from CSR were discussed. The explained statistical approach was used to investigate the effect of scanning speed parameter on the spatial distribution of gas pores in laser powder bed fusion manufactured stainless steel parts. Furthermore, and to highlight the impact of the spatial distribution of pores on mechanical properties, fatigue performances of parts with clustered and randomly distributed pores were simulated by finite element analysis. It is shown that by reducing the scanning speed, the spatial distribution of gas pores deviated more from CSR, and correspondingly fatigue performance deteriorated.

36 MATERIALS SCIENCE↗

DOE OSTI2011

A HIGH SPATIAL RESOLUTION STUDY OF THE {lambda} = 3 mm CONTINUUM OF ORION-KL

Recent interferometric observations have called into question the traditional view of the Orion-KL region, which displays one of the most well-defined cases of chemical differentiation in a star-forming region. Previous lower-resolution images of Orion-KL show emission signatures for oxygen-bearing organic molecules toward the Orion Compact Ridge and emission for nitrogen-bearing organic molecules toward the Orion Hot Core. However, more recent observations at higher spatial resolution indicate that the bulk of the molecular emission arises from many smaller, compact clumps that are spatially distinct from the traditional Hot Core and Compact Ridge sources. It is this type of observational information that is critical for guiding astrochemical models, as the spatial distribution of molecules and their relation to energetic sources will govern the chemical mechanisms at play in star-forming regions. We have conducted millimeter-imaging studies of Orion-KL with various beam sizes using the Combined Array for Research in Millimeter-Wave Astronomy in order to investigate the continuum structure. These {lambda} = 3 mm observations have synthesized beam sizes of {approx}0.''5-5.''0. These observations reveal the complex continuum structure of this region, which stands in sharp contrast to the previous structural models assumed for Orion-KL based on lower spatial resolution images. The new results indicate that the spatial scaling previously used in determination of molecular abundances for this region are in need of complete revision. Here we present the results of the continuum observations, discuss the sizes and structures of the detected sources, and suggest an observational strategy for determining the proper spatial scaling to accurately determine molecular abundances in the Orion-KL region.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2022

Improving Simulations of Vegetation Dynamics over the Tibetan Plateau: Role of Atmospheric Forcing Data and Spatial Resolution

The efficacy of vegetation dynamics simulations in offline land surface models (LSMs) largely depends on the quality and spatial resolution of meteorological forcing data. In this study, the Princeton Global Meteorological Forcing Data (PMFD) and the high spatial resolution and upscaled China Meteorological Forcing Data (CMFD) were used to drive the Simplified Simple Biosphere model version 4/Top-down Representation of Interactive Foliage and Flora Including Dynamics (SSiB4/TRIFFID) and investigate how meteorological forcing datasets with different spatial resolutions affect simulations over the Tibetan Plateau (TP), a region with complex topography and sparse observations. By comparing the monthly Leaf Area Index (LAI) and Gross Primary Production (GPP) against observations, we found that SSiB4/TRIFFID driven by upscaled CMFD improved the performance in simulating the spatial distributions of LAI and GPP over the TP, reducing RMSEs by 24.3% and 20.5%, respectively. The multi-year averaged GPP decreased from 364.68 gC m –2 yr –1 to 241.21 gC m –2 yr –1 with the percentage bias dropping from 50.2% to –1.7%. When using the high spatial resolution CMFD, the RMSEs of the spatial distributions of LAI and GPP simulations were further reduced by 7.5% and 9.5%, respectively. This study highlights the importance of more realistic and high-resolution forcing data in simulating vegetation growth and carbon exchange between the atmosphere and biosphere over the TP.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Multiscale spatial analysis of fracture arrangement and pattern reconstruction using Ripley's K-function

This work presents novel multiscale spatial data analytics using Ripley's K-function, as a measure of spatial interaction, to study one-dimensional arrangement of fractures. Fracture spatial arrangements are classified into clustered, anticlustered, or indistinguishable from random by testing statistical significance of the calculated Ripley's K-function. Characterizations of fracture arrangements are performed as a function of length scale and position. Analysis of the K-function along the study interval identifies where fracture clustering and anticlustering occur. Here, a simulation technique is also introduced here to statistically reconstruct spatial arrangements and to generate fracture realizations that are spatially similar to the fractures observed in the field. With this simulation technique, one can also fill spatial gaps in fracture measurements where data are absent, unreliable, or unused. Synthetic as well as field-measured 1D fracture datasets are used for testing and demonstration. Methods introduced in this work can be readily applied to fracture datasets observed in outcrops, borehole image logs, and cores.

58 GEOSCIENCES↗

DOE OSTI2023

Spatially Resolved Top-Down Proteomics of Tissue Sections Based on a Microfluidic Nanodroplet Sample Preparation Platform

Conventional proteomics measures the averaged signal from mixed cell populations or bulk tissues, leading to the dilution of significant changes in subpopulations of cells that might serve as important biomarkers. Recent developments in bottom-up proteomics have enabled spatial mapping of cellular heterogeneity in tissue microenvironments. However, bottom-up proteomics cannot precisely infer the abundance changes of intact proteins, which are presented as proteoforms. Herein, we described a spatially resolved top-down proteomics (TDP) platform for proteoform identification and quantification directly on thin tissue sections. The spatial TDP platform consisted of a nanoPOTS (nanodroplet Processing in One pot for Trace Samples)-based sample preparation system and an LCM (laser capture microdissection)-based cell isolation system. We improved the nanoPOTS sample preparation by adding benzonase in the extraction buffer to enhance the coverage of nucleus proteins. Using ~200 cultured cells as model samples, the improved approach increased proteoform identifications from 493 to 700; newly identified proteoforms primarily corresponded to nuclear proteins. To demonstrate the spatial TDP platform in tissue samples, we analyzed LCM-isolated tissue voxels from rat brain cortex and hypothalamus regions. We quantified 426 proteoforms by combining identifications from TopPIC and TDPortal with the quantitation from ProMex. Several proteoforms corresponding to the same gene exhibited mixed abundance profiles between two tissue regions, suggesting potential PTM-specific spatial distributions. The spatial TDP workflow has prospects for biomarker discovery at proteoform level from small tissue sections.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

Spectroscopy-based isotopic (δ 13 C) analysis for high spatial resolution of carbon exchange in the rhizosphere

The rhizosphere is a highly dynamic zone bridging plant roots with needed nutrient resources in soil. While the rhizosphere may be small, it has a disproportionally large impact on plant success and biomass production. A suite of rhizosphere-hosted microbial and geochemical interactions facilitate nutrient acquisition by plant roots, and, in turn, the roots stimulate these processes by supplying organic carbon into the rhizosphere. The small physical dimensions of the rhizosphere, however, can constrain efforts to elucidate key carbon exchange processes and their spatial extent and localization. We present a method for spatially resolved δ 13 C analysis of rhizosphere samples by coupling laser ablation (LA) sampling with isotopic analysis using capillary absorption spectroscopy (CAS) which differs from conventional mass spectrometer (MS) approaches. The CAS system has high sensitivity (requires fewer nanomoles of CO 2 per analysis) than comparable MS systems, which enables reduced sample size requirements to thereby improve spatial resolution (from 25 μm to as low as a projected 5 μm spatial resolution). We demonstrate the utility of CAS using rhizosphere samples from switchgrass plants exposed to 13 CO 2 . As a result, this technique will provide a capability for tracking the extent and spatial distribution of root exudate into the rhizosphere at highly detailed spatial scales.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

Long-term spatial and temporal solar resource variability over America using the NSRDB version 3 (1998–2017)

The study assesses the long-term spatial and temporal solar resource variability in America using the 20-year National Renewable Energy Laboratory's (NREL's) National Solar Radiation Database (NSRDB). Specifically, the coefficient of variation (COV) is used to analyze the spatial and temporal (interannual and seasonal) variability. Further, both spatial and temporal long-term variability are analyzed using the Köppen-Geiger climate classification. The temporal variability is found that, on average, the continental United States (CONUS) COV reaches up to 5% for global horizontal irradiance (GHI) and 10% for direct normal irradiance (DNI), and that the NSRDB domain's COV is roughly twice that of CONUS. For the seasonal variability analysis, the winter months are found to exhibit higher COV than the other seasons. In particular, December exhibits the highest variability, reaching on average 30% for DNI and 20% for GHI over various areas. On the other hand, the summer months demonstrate significantly lower variability, reaching only less than 20% for DNI and 10% for GHI, on average. Similarly, the spatial variability is analyzed by comparing each pixel to its neighbors. The long-term spatial variability is found to increase with the number of neighboring pixels being considered, which is equivalent to an increase in distance (within a 100-km x 100-km square grid). As expected, the DNI spatial variability is higher than that of GHI. Moreover, the annual solar irradiance anomalies are found to reach ±25% for both GHI and DNI (and even exceed those value in some instances) during each year of the 20-year period.

14 SOLAR ENERGY↗

DOE OSTI2016

Spatially resolved measurements to improve analytical performance of solution-cathode glow discharge optical-emission spectrometry

We report past studies of the solution-cathode glow discharge (SCGD) revealed that elemental and molecular emission are not spatially homogenous throughout the source, but rather conform to specific zones within the discharge. Exploiting this inhomogeneity can lead to improved analytical performance if emission is collected only from regions of the discharge where analyte species emit strongly and background emission (from continuum, elemental and/or molecular sources) is lower. Effects of this form of spatial discrimination on the analytical performance of SCGD optical emission spectrometry (OES) have been investigated with an imaging spectrograph for fourteen atomic lines, with emphasis on detection limits and precision. Vertical profiles of the emission intensity, signal-to-background ratio, and signal-to-noise ratio were collected and used to determine the optimal region to view the SCGD on a per-element basis. With optimized spatial filtering, detection limits ranged from 0.09–360 ppb, a 1.4–13.6 fold improvement over those obtained when emission is collected from the full vertical profile (1.1– 840 ppb),with a 4.2-fold average improvement. Precision was found to be unaffected by spatial filtering, ranging from0.5–2.6% relative standard deviation (RSD) for all elements investigated, closely comparable to the 0.4–2.4% RSD observed when no spatial filtering is used. In conclusion, spatial profiles also appear useful for identifying optimal line pairs for internal standardization and for flagging the presence of matrix interferences in SCGD-OES.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2012

Investigation of a polychromatic tomography method for the extraction of the three-dimensional spatial structure of implosion core plasmas

A spectroscopic method has been developed to extract the three-dimentional spatial structure (i.e., spatial distribution of electron temperature and density) of inertial confinement fusion implosion cores based on the analysis of space-resolved spectra from a tracer element recorded along three quasi-orthogonal lines of sight. We discuss a spectral model that computes space-resolved spectra for a given spatial structure. This model is then combined with a multi-objective search and optimization technique driven by a Pareto genetic algorithm to perform the inversion and to extract the spatial structure of the implosion core from a simultaneous and self-consistent analysis of a set of space-resolved spectra. This method is investigated with a series of synthetic data test cases to explore its reliability, requirements, and limitations. We have found a constraint parameter P{sub const} such that the method is robust and the extracted spatial structure is reliable when P{sub const}> 1. The idea of polychromatic tomography is general and has potential to extract the spatial structure of other laboratory high energy-density plasmas.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2014

Calculation of the spatial resolution in two-photon absorption spectroscopy applied to plasma diagnosis

We report a detailed characterization of the spatial resolution provided by two-photon absorption spectroscopy suited for plasma diagnosis via the 1S-2S transition of atomic hydrogen for optogalvanic detection and laser induced fluorescence (LIF). A precise knowledge of the spatial resolution is crucial for a correct interpretation of measurements, if the plasma parameters to be analysed undergo strong spatial variations. The present study is based on a novel approach which provides a reliable and realistic determination of the spatial resolution. Measured irradiance distribution of laser beam waists in the overlap volume, provided by a high resolution UV camera, are employed to resolve coupled rate equations accounting for two-photon excitation, fluorescence decay and ionization. The resulting three-dimensional yield distributions reveal in detail the spatial resolution for optogalvanic and LIF detection and related saturation due to depletion. Two-photon absorption profiles broader than the Fourier transform-limited laser bandwidth are also incorporated in the calculations. The approach allows an accurate analysis of the spatial resolution present in recent and future measurements.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2018

Methods of improving spatial resolution for IR spectroscopy in atmospheric-pressure plasma systems

High-temperature, atmospheric pressure plasma systems operated in molecular gases present complex diagnostic challenges. Infrared spectroscopy has been used to make measurements of the absorbance spectrum of complex molecular gas mixtures and thereby calculate the concentrations and species temperatures in these systems. For atmospheric-pressure systems, high spatial gradients arise and high spatial-resolution measurements are thus desirable. Some systems have achieved increased spatial resolution by reducing the beam diameter. However, this increase in spatial resolution comes at the expense of the optical throughput. Here we propose modifying a commercial Fourier Transform, Infrared spectrometer system with a set of simple optical elements. This design allows for scannable, high spatial resolution absorbance spectrum measurements of a complex molecular gas mix. We analyze the signal-to-noise ratio for this design and compare it with the diameter reduction method. For sufficiently low plasma radiation levels, this design improves the signal-to-noise ratio relative to reducing the beam diameter size while transmitting nearly all of the beam power. Furthermore, whether this design can achieve the desired sub-mm spatial resolution depends on the radiating area of the infrared light source in the spectrometer.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2019

Learning Temporal and Spatial Correlations Jointly: A Unified Framework for Wind Speed Prediction

Leveraging both temporal and spatial correlations to predict wind speed remains one of the most challenging and less studied areas of wind speed prediction. In this paper, the problem of predicting wind speeds for multiple sites is investigated by using the spatio-temporal correlation. We proposed a deep architecture termed predictive spatio-temporal network (PSTN), which is a unified framework integrating a convolutional neural network (CNN) and a long short-term memory (LSTM). Initially, the spatial features are extracted from the spatial wind speed matrices (SWSMs) by the CNN at the bottom of the model. Then, the LSTM captures the temporal dependencies among the spatial features extracted from contiguous time points. Lastly, the predicted wind speeds are given by the last state of the top layer of the LSTM, which are generated by using the spatial features and temporal dependencies. Though composed of two kinds of architectures, PSTN is trained with one loss function in an end-to-end (E2E) manner, which can learn temporal and spatial correlations jointly. Experiments for short-term predictions are conducted on real-world data, whose results demonstrate that PSTN outperforms prior methods.

42 ENGINEERING↗

DOE OSTI2019

Modeling spatial dependence and economic hotspots in landowners’ willingness to supply bioenergy crops in the northeastern United States

This paper investigates the spatial heterogeneity of landowners’ willingness to supply three bioenergy crops: switchgrass, Miscanthus , and willow, in the northeastern United States. Spatial heterogeneity might arise for several reasons. For example, landowners closer to bioenergy processing plants might be more likely to be willing to supply bioenergy crops, and landowners who are more willing to supply bioenergy crops may be spatially clustered because they share similar land attributes, demographics, experiences, and/or values. Using high–resolution GIS data related to the location of pellet plants utilizing bioenergy crops and survey data related to landowners’ characteristics including spatial location, we estimate a spatial probit model to explain the variation in individual–specific reservation prices (RPs)—the feedstock price at which landowners become willing to supply a bioenergy crop. We find that respondents’ RP is lower the closer they live to their nearest pellet plant and spatial dependency is only present for switchgrass supply. We also identify three economic hotspots (areas with high potential supply and low RPs) for each bioenergy crop. We believe that bioenergy supply chains could be developed around these hotspots.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2019

The spatial scale dependence of diazotrophic and bacterial community assembly in paddy soil

Aim: The factors driving microbial community β-diversity (variation in composition) at different spatial scales yield fundamental insights into the mechanisms that maintain ecosystem biodiversity, which as yet are uncertain. In this work, we explore whether spatial scale-dependent patterns of β-diversity vary between microbial functional groups and bacterial taxa (i.e., diazotrophic and bacterial communities) across local to regional scales (from metres to hundreds of kilometres). Location: Eastern China. Time period: October and November 2015. Major taxa studied: Diazotrophic and bacterial communities. Methods: We use two complementary statistical tools to unveil biotic mechanisms (i.e., species association) underlying variation in β-diversity of diazotrophic and bacterial communities. We examined distance–decay slopes of both communities at the local (1–113 m), meso- (3.4–39 km) and regional (103–668 km) scales. We used an environmentally constrained checkerboard score and topological features of association networks as indices of species association. We then calculated contributions of species association, abiotic factors and geographical distance to explain community β-diversity. The scale-dependent distance–decay relationships were also examined in ubiquitous (high occupancy across samples) and endemic communities of diazotrophs and bacteria. Results: Diazotrophs displayed steeper distance–decay slopes than bacteria, suggesting that the β-diversity of diazotrophic communities was more variable. The distance–decay slopes were dependent on spatial scales in both communities, owing to different contributions of geographical distance, abiotic factors and species association at three spatial scales. Intriguingly, species association was greater and contributed more to community β-diversity than other forces at the local scale, implying that species association could greatly alter community structures. Main conclusions: Drivers of diazotrophic and bacterial community β-diversity depended on spatial scales, resulting in different distance–decay patterns. Moreover, this was the first study to use two methods to demonstrate that species association played important, but as yet unrecognized, roles in driving spatial scale-dependent β-diversity.

54 ENVIRONMENTAL SCIENCES↗